http://www.cnr.it/ontology/cnr/individuo/prodotto/ID209915

Phonetically-Based Multi-Layered Neural Networks for Vowel Classification (Articolo in rivista)

Type

Prodotto della ricerca (Classe)
Articolo in rivista (Classe)

Label

Phonetically-Based Multi-Layered Neural Networks for Vowel Classification (Articolo in rivista) (literal)

Anno

1990-01-01T00:00:00+01:00 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi

10.1016/0167-6393(90)90041-7 (literal)

Alternative label

Cosi P., Bengio Y., De Mori R. (1990)
Phonetically-Based Multi-Layered Neural Networks for Vowel Classification
in Speech communication (Print); Elsevier, Amsterdam (Paesi Bassi)
(literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori

Cosi P., Bengio Y., De Mori R. (literal)

Pagina inizio

15 (literal)

Pagina fine

29 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#altreInformazioni

ISSN: 01676393 CODEN: SCOMD Source Type: Journal Original language: English Document Type: Article (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url

http://www.sciencedirect.com/science/article/pii/0167639390900417 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume

9 (literal)

Rivista

Speech communication (Rivista)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali

15 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo

1 (literal)

Note

S (literal)
Science direct - Elsev (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni

Piero Cosi Centro di Studio per le Ricerche di Fonetica, C.N.R., Piazza Salvemini, 13, 35131 Padova, Italy (now ISTC CNR - UOS Padova) Yoshua Bengio School of Computer Science, McGill University, 805 Sherbrooke Str. W., Montreal, Quebec, Canada H3A 2K6 Renato De Mori Centre de Recherche en Informatique de Montreal, 1550, de Maisonneuve Blvd. W., Montreal, Quebec, Canada H3G 1N2 (literal)

Titolo

Phonetically-Based Multi-Layered Neural Networks for Vowel Classification (literal)

Abstract

The vowel sub-component of a speaker-independent phoneme classification system will be described. The architecture of the vowel classifier is based on an ear model followed by a set of Multi-Layered Neural Networks (MLNN). MLNNs are trained to learn how to recognize articulatory features like the place of articulation and the manner of articulation related to tongue position. Experiments are performed on 10 English vowels showing a recognition rate higher than 95% on new speakers. When features are used for recognition, comparable results are obtained for vowels and diphthongs not used for training and pronounced by new speakers. This suggests that MLNNs suitably fed by the data computed by an ear model have good generalization capabilities over new speakers and new sounds. (literal)
Nous présentons un système de classification de phonèmes indépendant du locuteur et appliqué aux voyelles. L'architecture du classificateur de voyelles est basée surun modèle d'oreille suivi d'un ensemble de réseaux neuronaux à plusieurs couches (MLNN). Les MLNNs apprennent à reconnaître les traits articulatoires, par exemple le lieu et le mode d'articulation en relation avec la position de la langue. Des expériences ont été effectuées sur 10 voyelles anglaises et montrent un taux de reconnaissance supérieur à 95% sur de nouveaux locuteurs. Lorsque les traits sont utilisés pour la reconnaissance, des résultats comparables sont obtenus pour des voyelles et des dihthongues qui n'ont pas été utilisées lors de l'apprentissage et prononcées par de nouveaux locuteurs. Ceci suggère que, pour des données calculées par un modèle d'oreille, les MLNNs présentent un bon pouvoir de généralisation pour de nouveaux locuteurs et de nouveaux sons. (literal)

Editore